In an asset-intensive industry, the value of equipment, as a fraction of revenue, is commonly high. The financial impact of managing and maintaining such equipment, therefore, can be significant in such industries. Existing approaches for management of high-value machinery such as, for example, heavy industrial vehicles, include performing periodic maintenance according to a static pre-determined schedule. However, such approaches are based on assumptions that do not apply in many situations.
Accordingly, a need exists for techniques to design and develop a cumulative wear-based indicator of future premature vehicular component failures by combining different sources of data.